(636e) Influence of Solution Environment on Estimation of Ionic Conductivity in Ionic Liquid Electrolytes

The growing need for highly conductive, low volatility, high performance electrolytes for energy storage devices and electrochemical applications has motivated the development of ionic liquid (IL) electrolytes. Dilution of ILs with molecular solvents is known to improve the performance of such electrolytes, yielding highly non-monotonous trends in ionic conductivity as a function of the IL concentration. Although molecular simulations have been used to predict these trends, the accuracy of such predictions is dependent on the force field, and the formalism employed to estimate the conductivity. Differences in atomic charges between force fields introduce variations in the organization of ions at the microscopic level, translating to significant differences in the bulk conductivity estimates. On the other hand, the use of Nernst-Einstein and Einstein formalisms can yield different conductivity trends due to the differences in the treatment of the ion-ion interactions. This work systematically investigates the effect of three charge manipulation strategies – charge scaling, use of virtual site, and DFT based charge optimization, in predicting the ionic conductivity of a mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate ([C₂mim][BF₄]) in ethylene glycol (EG). We also compare the conductivity trends predicted using both Nernst-Einstein and Einstein formalisms to account for the ion-ion interactions at 10 different concentrations. We provide an insight into the different ion transport mechanisms that evolve due to the microscopic re-organization of the ions at different IL concentrations. Lastly, we propose a workflow that helps overcome these challenges and predict ionic conductivity trends in electrolytes based on ILs and molecular solvents.